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For the last few years, big data has degenerated into a big topic of discussion, not just in the tech world where it started but also in the corporate world, development space, and government. It is set to grow as information (valuable contextualized data) and data (facts or figures) are now everywhere, and the sources continue to increase daily.

Still, there is a lot of confusion about what it actually means.

This article discusses big data, why everyone is talking about it, and its comparison to research-based data.

 

What is big data?

Big data is a combination of structured, semi-structured, and unstructured data collected by organizations that can be mined for information and used in machine learning projects, predictive modeling, and other advanced analytics applications.

Oracle describes big data as data that contains greater variety, arriving in increasing volumes and with more velocity (the three Vs).

According to Tech Target, big data doesn’t equate to any specific volume of data. Still, big data deployments typically involve terabytes, petabytes, and even exabytes of data created and collected over time.

How do big data and research data compare and contrast?

Whereas big data can tell us what has happened in the past and can make predictions on future events, in itself, it cannot explain “why” it happened. Research data that is more targeted to a specific question and can also include qualitative data can answer “why,” which is a fundamental difference between the two types of research.

Actually, research data can form an essential part of further enriching data sets that contribute to big data.

Rather than getting caught up in the semantics of the differences and which is more effective, the more important question is not how much data you have but what you do with it. The value of information obtained from the two methods of data collection and analysis (whether from large volumes of unstructured data – big data or research-based data) determines the most effective one for your business.

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If we were to focus on the desired outcome of a data-gathering project by its ability to drive decision-making, then we see that both big data and data from research are just a means through which we get an outcome. Various other considerations must be made for each, such as the availability of the data in the first place, big data analysis capabilities, and the cost of marketing research, among others.

While research aims at solving particular research questions, big data tries to make sense of the information available where the topic/research question may or might not be in context, ergo sometimes the need to have ‘influencers’ to drive the topic.

Big data takes a lot of time to get output from, while research could be scoped to a particular time, reducing the time it takes to get the insights needed to make a business decision.

What does big data mean for market research data?

The question as to whether big data will render data from direct market research obsolete continues to elicit colossal debate in the research industry. In the developed world, big data is, in many aspects, providing insights that, traditionally, only market research could. In emerging markets, there may not be as much data to prompt the concern about the continued primary role and need for market research.

To answer this question, one must first contemplate the role of primary or secondary research in today’s world. The role of market research is to understand consumer behavior and perception and measure the consumption of goods or services. Or, simply put, the “hows” and the “whys.”

Without context and connections, big data is useless. It is simply megabytes of data that need to be compiled and manipulated to answer a question.

Humans are irrational, and more often than not, why they behave in a certain way or make the decisions they make cannot be explained or even predicted. No amount of machine intelligence through big data analytics will be able to answer the “whys.”

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Big data can’t replace the need for market research. Instead, the two complement each other, as each has its purpose and benefits that, if used correctly, can effectively understand consumer behavior.

So, which is more important?

Organizations still strive to understand and respond to shifts in demand and consumer preferences. By having a clear understanding of the capabilities and challenges of big data and research data, businesses can effectively plan their expenditure either on tools to collect and analyze their own big data or go the primary research route by engaging a research agency such as GeoPoll to get a complete picture of consumer behavior and preferences.

Talk to us about your data and research needs.